Information processing apparatus and information processing method

ABSTRACT

An information processing apparatus according to an embodiment of the present technology includes a detection unit, an estimation unit, and a prediction unit. The detection unit detects a target object from an input image. The estimation unit estimates a posture of the detected target object. The prediction unit predicts an action of the target object on a basis of the estimated posture.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a U.S. National Phase of International PatentApplication No. PCT/JP2016/003877 filed on Aug. 25, 2016, which claimspriority benefit of Japanese Patent Application No. JP 2015-191187 filedin the Japan Patent Office on Sep. 29, 2015. Each of theabove-referenced applications is hereby incorporated herein by referencein its entirety.

TECHNICAL FIELD

The present technology relates to an information processing apparatus,an information processing method, and a program for raising caution of adriver when driving an automobile or the like, for example.

BACKGROUND ART

Accident avoidance technologies at emergencies, such as an automaticemergency brake and a collision avoidance system, are becoming common.Further, systems that raise caution of drivers and the like for avoidingaccidents have also been developed. For example, in Patent Literature 1,a judgment is made on whether a bicycle traveling in front of ownvehicle is apt to fall over on the basis of weather information and roadinformation read out from a database. In a case where the bicycle infront is apt to fall over, a warning to that effect is made to a driver(paragraphs [0043] to [0049] etc. in specification of Patent Literature1).

In addition, in Patent Literature 2, a judgment is made on whether apedestrian is present in an area where own vehicle travels by analyzingan image ahead of the vehicle, that has been photographed by an infraredcamera. Also by detecting movements of pedestrians outside the travelingarea, a danger level of a pedestrian entering the traveling area isjudged. A warning sound with a narrow directivity is output to apedestrian in an area or a pedestrian of a high danger level (paragraphs[0051], [0052], [0068], etc. in specification of Patent Literature 2).

CITATION LIST Patent Literature

Patent Literature 1: Japanese Patent Application Laid-open No.2009-122854

Patent Literature 2: Japanese Patent Application Laid-open No.2014-52883

DISCLOSURE OF INVENTION Technical Problem

As described above, there is a demand for a technology capable ofraising caution of a driver and the like by providing effectiveinformation for preventing an accident or the like from occurring.

In view of the circumstances as described above, the present technologyaims at providing an information processing apparatus, an informationprocessing method, and a program that are capable of providing effectiveinformation so as to raise caution.

Solution to Problem

To attain the object described above, an information processingapparatus according to an embodiment of the present technology includesa detection unit, an estimation unit, and a prediction unit.

The detection unit detects a target object from an input image.

The estimation unit estimates a posture of the detected target object.

The prediction unit predicts an action of the target object on a basisof the estimated posture.

In this information processing apparatus, the action of the targetobject can be predicted highly accurately on the basis of the estimatedposture. As a result, it becomes possible to provide effectiveinformation for preventing an accident or the like from occurring to adriver and the like so as to raise caution.

The detection unit may be capable of detecting a pedestrian from theinput image. In this case, the prediction unit may predict an action ofthe pedestrian on a basis of an estimated posture of the pedestrian.

Accordingly, it becomes possible to prevent an accidental contact with apedestrian, for example, or the like from occurring.

The detection unit may be capable of detecting a two-wheeled vehicle anda rider thereof from the input image. In this case, the estimation unitmay estimate at least a posture of the rider. Further, the predictionunit may predict an action of the two-wheeled vehicle and the riderthereof on a basis of the estimated posture of the rider.

Accordingly, it becomes possible to prevent an accidental contact with atwo-wheeled vehicle, for example, or the like from occurring.

The estimation unit may estimate a posture of the two-wheeled vehicle.In this case, the prediction unit may predict the action of thetwo-wheeled vehicle and the rider thereof on a basis of the estimatedposture of each of the two-wheeled vehicle and the rider thereof.

Accordingly, the actions of the two-wheeled vehicle and the riderthereof can be predicted highly accurately.

The prediction unit may calculate a feature point related to the targetobject on a basis of the estimated posture, and predict the action ofthe target object on a basis of a position of the calculated featurepoint.

Accordingly, the action of the target object can be predicted easily.

The feature point may be a barycenter point of the target object.

By using a position of the barycenter point, the action of the targetobject can be predicted highly accurately.

The detection unit may be capable of detecting a two-wheeled vehicle anda rider thereof from the input image. In this case, the prediction unitmay calculate, as the feature point, a barycenter point of the rider oran overall barycenter point of the two-wheeled vehicle and the riderthereof.

The prediction unit may calculate one or more contact points of thetarget object with a road surface on a basis of the estimated posture,and predict the action on a basis of a relative positional relationshipbetween the feature point and the one or more contact points.

Accordingly, the action of the target object can be predicted highlyaccurately.

The prediction unit may predict a movement direction of the targetobject.

Accordingly, an accidental contact with the target object, and the likecan be prevented from occurring.

The prediction unit may predict an abrupt acceleration of the targetobject.

Accordingly, an accidental contact with the target object, and the likecan be prevented from occurring.

The estimation unit may estimate a framework of the detected targetobject.

Accordingly, the posture of the target object can be estimated highlyaccurately.

The information processing apparatus may be mounted on a mobile objectapparatus, and the information processing apparatus may further includean output unit that generates and outputs danger avoidance informationfor avoiding a danger related to a drive of the mobile object apparatuson a basis of the predicted action of the target object.

Accordingly, it becomes possible to raise caution of a driver of themobile object apparatus and prevent an accidental contact with apedestrian and the like from occurring.

The output unit may judge a possibility of the mobile object apparatusand the target object coming into contact with each other, and outputinformation on the judged possibility.

Accordingly, it becomes possible to raise caution of the driver andprevent an accidental contact with a pedestrian and the like fromoccurring.

The prediction unit may be capable of predicting a movement direction ofthe target object. In this case, the output unit may output an imageincluding the predicted movement direction.

Accordingly, it becomes possible to raise caution against a pedestrianor the like approaching a path of the mobile object apparatus, forexample, and prevent an accidental contact or the like from occurring.

The output unit may output an image including a dangerous area wherethere is a possibility that the mobile object apparatus and the targetobject will come into contact with each other.

Accordingly, it becomes possible for the driver to easily grasp a safepath and the like, for example.

An information processing method according to an embodiment of thepresent technology is an information processing method executed by acomputer, the method including detecting a target object from an inputimage.

A posture of the detected target object is estimated.

An action of the target object is predicted on a basis of the estimatedposture.

A program according to an embodiment of the present technology causes acomputer to execute the following steps.

The step of detecting a target object from an input image.

The step of estimating a posture of the detected target object.

The step of predicting an action of the target object on a basis of theestimated posture.

Advantageous Effects of Invention

As described above, according to the present technology, it becomespossible to provide effective information to raise caution. It should benoted that the effects described herein are not necessarily limited, andany effect described in the present disclosure may be obtained.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 An outer appearance diagram showing a configuration example of anautomobile as an example of a mobile object apparatus on which a dangeravoidance apparatus according to a first embodiment is mounted.

FIG. 2 A block diagram showing a configuration example of the automobileshown in FIG. 1.

FIG. 3 A block diagram showing a functional configuration example of thedanger avoidance apparatus.

FIG. 4 A diagram for explaining an example of a framework estimation.

FIG. 5 A schematic diagram showing postures (frameworks) at a time apedestrian in a still state starts to walk in a predetermined direction(right-hand direction in figure).

FIGS. 6A, 6B, and 6C Schematic diagrams showing postures of otherpatterns when starting to walk from the still state.

FIG. 7 A schematic diagram showing (frameworks) at a time a bicyclerunning straight and a rider thereof change directions in apredetermined direction (right-hand direction in figure).

FIG. 8 A flowchart showing a processing example by the danger avoidanceapparatus.

FIG. 9 A flowchart showing an example of an operation of predictingmovement directions of a pedestrian and bicycle.

FIG. 10 A flowchart showing an example of pedestrian both-feetprocessing.

FIG. 11 A flowchart showing an example of pedestrian one-footprocessing.

FIG. 12 A flowchart showing an example of bicycle processing.

FIG. 13 A diagram showing an example of a danger avoidance image outputby a warning output unit.

FIG. 14 A diagram for explaining another embodiment of the actionprediction.

FIG. 15 A diagram for explaining another embodiment of the postureestimation of a bicycle and a rider thereof.

MODES FOR CARRYING OUT THE INVENTION

Hereinafter, embodiments of the present technology will be describedwith reference to the drawings.

First Embodiment

[Configuration of Automobile]

FIG. 1 is an outer appearance diagram showing a configuration example ofan automobile as an example of a mobile object apparatus on which adanger avoidance apparatus according to a first embodiment of thepresent technology is mounted. FIG. 2 is a block diagram thereof.

As shown in FIG. 1, an automobile 100 includes a distance sensor 10, afront camera 11, and an in-vehicle photographing camera 12. Further, asshown in FIG. 2, the automobile 100 includes a steering apparatus 15, abraking apparatus 16, a vehicle body acceleration apparatus 17, asteering angle sensor 20, a wheel speed sensor 21, a brake switch 22, anaccelerator sensor 23, a control unit 30, a display apparatus 35, and adanger avoidance apparatus 40.

For example, the distance sensor 10 is provided at substantially thecenter of a front portion of the automobile 100, and detects informationon a distance between the automobile 100 and an object present in amovement direction thereof. The distance sensor 10 includes varioussensors that use a millimeter wave radar, infrared laser, and the like,for example.

The front camera 11 is provided in a cabin or roof portion of theautomobile 100, for example, and photographs a front view of theautomobile 100 at a predetermined frame rate. The photographed imagephotographed by the front camera 11 is output to the danger avoidanceapparatus 40 via the control unit 30, and a movement of a target objectpresent in front of own vehicle is predicted. For example, the frontcamera 11 includes an image sensor that uses a CMOS, a CCD, or the like.

As shown in FIG. 1, in this embodiment, a pedestrian 2, a bicycle 3, anda rider 4 thereof will be exemplified as the target object 1. Inaddition, the present technology is also applicable to riders of othertwo-wheeled vehicles such as a motorcycle, an auto three-wheeledvehicle, and the like.

The in-vehicle photographing camera 12 is provided in the cabin of theautomobile 100 and photographs an inside of the cabin at a predeterminedframe rate. A presence or absence of a passenger, a sitting positionthereof, and the like, for example, can be judged by the imagephotographed by the in-vehicle photographing camera 12.

It should be noted that the distance sensor 10, the front camera 11, andthe in-vehicle photographing camera 12 may be configured such thatoutputs thereof are supplied to the danger avoidance apparatus 40instead of the control unit 30 as shown in FIG. 2.

The steering apparatus 15 is typically constituted of a power steeringapparatus and transmits a steering operation of the driver to steeredwheels. The braking apparatus 16 includes a brake actuator attached toeach wheel and a hydraulic circuit for actuating the brake actuators,and controls a braking force of each of the wheels. The vehicle bodyacceleration apparatus 17 includes a throttle valve, a fuel injectionapparatus, and the like, and controls a rotational acceleration of drivewheels.

The control unit 30 controls operations of the respective mechanismsmounted on the automobile 100. For example, the control unit 30 controlsbrake, steering, acceleration, and the like of the automobile 100 on thebasis of operations of the driver using a steering wheel, anaccelerator, and the like. For example, the control unit 30 detects asteering amount and a steering direction on the basis of an output ofthe steering angle sensor 20 that detects a steering operation of thedriver, to thus control the steering apparatus 15.

Further, the control unit 30 calculates a vehicle body speed of thevehicle on the basis of an output of the wheel speed sensor 21 providedon all the wheels or a part of the wheels, and controls the brakingapparatus 16 such that locking (slipping) of the wheels is preventedfrom occurring. Furthermore, the control unit 30 controls the vehiclebody acceleration apparatus 17 on the basis of an output of theaccelerator sensor 23 that detects an accelerator pedal operation amountof the driver.

The brake switch 22 is used for detecting a brake operation (depressionof brake pedal) of the driver and is referenced in performing ABScontrol or the like.

The control unit 30 may control the steering apparatus 15, the brakingapparatus 16, and the vehicle body acceleration apparatus 17individually, as well as cooperatively control a plurality of them. As aresult, it becomes possible to control the automobile 100 to a desiredposture during steering (turning), braking, acceleration, and the like.

Further, the control unit 30 is configured to be capable of controllingthe steering apparatus 15, the braking apparatus 16, and the vehiclebody acceleration apparatus 17 irrespective of the various operations ofthe driver described above. For example, the automobile 100 may includean automatic driving function. In this case, the control unit 30subjectively controls the respective apparatuses on the basis of theoutputs of the respective sensors and cameras.

The display apparatus 35 includes a display unit that uses liquidcrystal, EL (Electro-Luminescence), or the like, for example, anddisplays map information, navigation information, and the like on thedisplay unit. Further, the display apparatus 35 displays a dangeravoidance image output from the danger avoidance apparatus 35.Typically, a car navigation apparatus is used as the display apparatus35. Further, an apparatus that causes an AR (Augmented Reality) image tobe displayed at a predetermined position of a windshield or the like mayalso be used.

As will be described later in detail, the danger avoidance apparatus 40detects the target object 1 from an image photographed by the frontcamera 11 and predicts an action of the target object 1. In other words,the next action of the target object 1 that may be made in the futuresince the photographed timepoint is predicted. For example, a movementdirection of the target object 1, presence or absence of an abruptacceleration, and the like can be predicted.

The danger avoidance apparatus 40 corresponds to an informationprocessing apparatus according to this embodiment and includes hardwarerequisite for a computer, such as a CPU, a RAM, and a ROM, for example.A danger avoidance method (information processing method) according tothe present technology is executed by the CPU loading a programaccording to the present technology, that is recorded in advance in theROM, to the RAM and executing it.

A specific configuration of the danger avoidance apparatus 40 is notlimited, and PLD (Programmable Logic Device) such as FPGA (FieldProgrammable Gate Array) and other devices such as ASIC (ApplicationSpecific Integrated Circuit) may also be used. Further, the dangeravoidance apparatus 40 may be configured as a part of the control unit30.

FIG. 3 is a block diagram showing a functional configuration example ofthe danger avoidance apparatus 40. The danger avoidance apparatus 40includes an image acquisition unit 41, an object detection unit 42, aposture estimation unit 43, an object movement prediction unit 44, anown-vehicle movement prediction unit 45, a danger level judgment unit46, and a warning output unit 47. For example, the CPU of the dangeravoidance apparatus 40 executes a predetermined program so as toconfigure each of the functional blocks.

The image acquisition unit 41 acquires an image photographed by thefront camera 11 (hereinafter, this image will be referred to as inputimage). The object detection unit 42 detects each of the pedestrian 2,the bicycle 3, and the rider 4 thereof from the input image. Thedetection of the pedestrian 2 and the bicycle 3 may be performed by anarbitrary image analysis technology such as template matching and imagescanning.

The posture estimation unit 43 estimates postures of the detectedpedestrian 2 and the rider 4. The posture estimation unit 43 includes apart estimation unit 48 and a framework detection unit 49. In otherwords, in this embodiment, the postures are judged on the basis offramework positions of the pedestrian 2 and the rider 4.

FIG. 4 is a diagram for explaining an example of the frameworkestimation. The framework of each of the pedestrian 2 and the rider 4 isexpressed by white circles 50, lines 51 connecting them, and a headportion 52 in FIG. 4. In FIG. 4, a barycenter point 55 of each of thepedestrian 2 and the rider 4, grounding points 56 of the pedestrian 2and the bicycle 3 with a road surface R, and xy coordinate systems areshown. These are used for predicting the next action of the pedestrian 2and the like.

The framework estimation is also called bone estimation or skeletonestimation and can be executed using a well-known technology. Describingan example thereof with reference to the pedestrian 2, first, a model ofa framework to be calculated, that is, a model of the positions andnumber of white circles 50, the lines 51 connecting them, and the likeshown in FIG. 4 are preset.

The framework model is typically set in accordance with an actualframework of a human body. For example, a plurality of main parts suchas a head, thigh, and waist are set, and the white circles 50 are set atbarycenter points of the parts and joint portions of the parts. Further,the lines 51 connecting the white circles 50 are set on the basis of thepositions of the parts.

On the basis of the image (partial image) of the pedestrian 2 in theinput image, the part estimation unit 48 calculates the position of eachpart set as described above with respect to the pedestrian 2 in theinput image. For example, images of each part in various postures arestored as template images. By matching these template images with theimage of the pedestrian 2, the position of each part of the pedestrian 2can be calculated.

The framework detection unit 49 sets the white circles 50, the lines 51,and the head portion 52 on the basis of the calculated position of eachpart, and thus the framework of the pedestrian 2 is detected.

It should be noted that it is also possible to mount a depth sensor on afront portion of the automobile 100 and estimate the position of eachpart using parameters obtained by machine learning on the basis of adepth image (distance image) of the pedestrian 2 obtained by the depthsensor. For example, one pixel in the image of the pedestrian 2 isselected, and depth information (distance information) of apredetermined area including the pixel is acquired. On the basis of thisdepth information, a judgment is made on which part the selected pixelis included in using the parameters described above. By executing thesame processing for each pixel in the image of the pedestrian 2, theposition of each part of the pedestrian 2 can be calculated.Accordingly, the framework of the pedestrian 2 is estimated.

Instead of the depth information, RGB information of a pixel may beused. Specifically, it is possible to judge a part including a selectedpixel using parameters obtained by machine learning on the basis ofperipheral RGB information of the selected pixel. In addition, theframework estimation may be executed by an arbitrary technology such asa method that uses a stereo camera. It should be noted that theframework of the rider 4 of the bicycle 3 can also be similarlyestimated.

The object movement prediction unit 44 predicts actions of thepedestrian 2 and the rider 4 of the bicycle 3 from the estimatedpostures, that is, the frameworks shown in FIG. 4. It should be notedthat in a case where the bicycle 3 and the rider 4 thereof are detectedas the target object 1, the actions of the bicycle 3 and the rider 4thereof are predicted on the basis of the postures of the bicycle 3 andthe rider 4 thereof.

Here, the postures of the bicycle 3 and the rider 4 thereof can beestimated on the basis of, for example, either the posture of the rider4 or the posture of the bicycle 3, or both of them. Further, the nextaction of the bicycle 3 and the rider 4 thereof includes both an actionof the rider 4 such as a steering operation and a pedal operation and anaction of the bicycle 3 such as moving straight, curving, and suddenacceleration. Hereinafter, the postures and actions of the bicycle 3 andthe rider 4 thereof may be described while merely referring to only therider 4 or the bicycle 3, such as a posture of the rider 4 and an actionof the bicycle 3.

The own-vehicle movement prediction unit 45 predicts the next action ofthe automobile 100. Typically, the own-vehicle movement prediction unit45 calculates a prediction path that the automobile 100 will advance in.For example, the prediction path is calculated from a current vehiclespeed, a steering amount, a steering direction, a rotationalacceleration of the drive wheels, or the like. Alternatively, theprediction path may be calculated on the basis of information on adestination set in a navigation apparatus or the like, current locationinformation acquired by a GPS or the like, map information, road trafficinformation, and the like. It should be noted that other actions of theautomobile 100 may also be predicted.

The danger level judgment unit 46 judges a danger level on the basis ofthe action of the target object 1 predicted by the object movementprediction unit 44, the prediction path predicted by the own-vehiclemovement prediction unit 45, and the like. Typically, a possibility ofan accidental contact, a collision accident, or the like occurringbetween the pedestrian 1 or the bicycle 3 (rider 4) and the automobile100 is judged. For example, in a case where the prediction path of theautomobile 100 overlaps or comes extremely close to a point ahead(extension) of the predicted movement direction of the pedestrian 2 orthe like, it is judged that the danger level is high.

Further, it is also possible to calculate the prediction path of thepedestrian 2 or the like on the basis of the predicted movementdirection of the pedestrian 2 or the like, and the like and judge thatthe danger level is high in a case where the prediction path overlaps orcomes extremely close to the prediction path of the automobile 100. Theprediction path of the pedestrian 2 or the like may be calculated by theobject movement prediction unit 44.

It is also possible to judge the danger level as high in a case where anabrupt acceleration of the bicycle 3 or the like is predicted and adirection of the abrupt acceleration is directed toward the predictionpath of the automobile 100. It should be noted that a danger leveljudgment similar to that described above may be executed while settingthe entire road that the automobile 100 is predicted to advance in asthe prediction path.

Instead of the prediction path, the position of the automobile 100 andthe position of the target object 1 at a predetermined timing in thefuture, typically a timing immediately after photographing, may each bepredicted so as to judge the danger level.

The warning output unit 47 outputs danger avoidance information foravoiding a danger concerning the drive of the automobile 100 on thebasis of the judged danger level. Specifically, information for avoidingan accidental contact or the like with the pedestrian 2 or the like isoutput. The danger avoidance information is output by, for example, animage, audio, or the like. The danger level judgment unit 46 and thewarning output unit 47 realize an output unit of this embodiment.

[Prediction of Action of Target Object]

The prediction of the next action based on a posture will be describedin detail. For example, various actions can be taken by the pedestrian 2and the bicycle 3, such as a leftward or rightward direction change whengoing straight, a change to another direction during a curve action, anda change in a static/dynamic state such as a sudden acceleration and asudden stop. A posture of the pedestrian 2 or the like at a time anaction is switched, such as the direction change and sudden accelerationdescribed above, that is, immediately before starting the next action,will be verified. In addition, features related to postures whenswitching to the next action are extracted for the various actions, tothus realize the action prediction of this embodiment.

Thus, the inventors of the present invention focused on the barycenterpoint 55 of the target object 1 and the grounding point 56 as thecontact point with the road surface R. Specifically, it was found thatat a time of a switch to the next action, the position of the barycenterpoint 55 and the relative positional relationship between the barycenterpoint 55 and the grounding point 56 change. By extracting the change inthe position of the barycenter point 55 or the like as a feature relatedto a posture at the time of a switch to the next action, the actionprediction according to the present technology described below wasdevised.

FIG. 5 is a schematic diagram showing postures (frameworks) at a timethe pedestrian 2 in a still state starts to walk in a predetermineddirection (right-hand direction in figure). The barycenter point 55, agrounding point 56L between a left foot FL and the road surface R, and agrounding point 56R between a right foot FR and the road surface R arecalculated in each posture. It should be noted that grounding pointlines L1 and L2 are set in a vertical direction from the groundingpoints 56L and 56R, respectively.

P1 in FIG. 5 shows a state where the pedestrian 2 stands still on bothfeet. P5 in FIG. 5 shows a state where the right foot FR (rear-side footregarding movement direction) moves one step forward, that is, a statewhere walking starts. P2 to P4 in FIG. 5 show postures during a switchfrom the still state to the walking state, that is, postures rightbefore starting to walk.

In P1 of FIG. 5, the barycenter point 55 is included in an area betweenthe grounding point lines L1 and L2. It should be noted that theposition on the grounding point line L1 and the position on thegrounding point line L2 are also within the area described above. In P2,the barycenter point 55 moves toward the right-hand side, that is, in awalking direction, in the area between the grounding point lines L1 andL2. In P3, the right foot FR (rear-side foot) is raised, and thus thereis one grounding point 56. The barycenter point 55 is positionedsubstantially above the grounding point 56L of the left foot FL(front-side leg), that is, on the grounding point line L1.

In P4 of FIG. 5, the weight moves as the right foot FR moves in themovement direction, and the barycenter point 55 moves more on theright-hand side (front side) than the grounding point line L1. In P5,the right foot FR takes a step forward, and the barycenter point 55 isthus included in the area between the grounding point lines L1 and L2.

FIGS. 6A, 6B, and 6C are schematic diagrams showing postures of otherpatterns when starting to walk from the still state. FIG. 6A is adiagram showing a case where the posture is tilted in the movementdirection (right-hand direction in figure) from the state of standing onboth feet, and the barycenter point 55 is moved out of the area betweenthe grounding point lines L1 and L2 on the right-hand side. In otherwords, the barycenter point 55 is deviated from the area between thegrounding point lines L1 and L2 in the movement direction.

FIG. 6B is a diagram showing a case where the left foot (front-sidefoot) takes a step forward from the state of standing on both feet. Whenthe left foot FL is raised from the road surface R, the number ofgrounding points 56 becomes one, and the barycenter point 55 is deviatedto the right-hand side of the grounding point line L2.

FIG. 6C is a diagram showing a case where the posture is tilted in themovement direction (right-hand direction in figure) from a state ofstanding only on the left foot FL, and the barycenter point 55 movesmore on the right-hand side than the grounding point line L1.

On the basis of P4 of FIG. 5 and FIGS. 6A 6B, and 6C, the inventorsfound the following points as the features of the posture immediatelybefore starting to walk, regarding walking from the still state.

The number of grounding points 56 is two (state of standing on bothfeet), and the barycenter point 55 is deviated from the area between thegrounding point lines L1 and L2. In this case, the pedestrian 2 startsto walk toward the side on which the barycenter point 55 is deviated.

The number of grounding points 56 is one (state of standing on onefoot), and the barycenter point 55 is not on the grounding point line(L1 or L2) and is deviated from the grounding point line. In this case,the pedestrian 2 starts to walk toward the side on which the barycenterpoint 55 is deviated.

It should be noted that although the case of walking in the right-handdirection in the figure has been described in the descriptions above,the same holds true in a case of walking in the left-hand direction inthe figure. Further, the present technology is not limited to the caseof starting to walk from the still state, and the prediction cansimilarly be performed also in a case where the pedestrian 2 walkingstraight in a direction vertical to the paper surface turns left orright.

FIG. 7 is a schematic diagram showing (frameworks) at a time the bicycle3 running straight and the rider 4 thereof change directions in apredetermined direction (right-hand direction in figure). The barycenterpoint 55 and the grounding point 56 between a wheel 3 a and the roadsurface R are calculated in each posture. The grounding point 56 can becalculated on the basis of, for example, an image of the bicycle 3detected from an input image. A grounding point line L extending in thevertical direction is set from the ground point 56.

P1 of FIG. 7 shows a state where the bicycle 3 is going straight towardthe front side in a direction vertical to the paper surface. P4 of FIG.7 shows a state where a handle 3 b is turned to the right-hand side,that is, the left-hand side when viewed from the rider 4, and is a statewhere the direction is started to be turned in the right-hand direction.P2 and P3 of FIG. 7 show postures at the time of a switch to the statewhere the direction is changed from the forward direction and is aposture right before turning the handle 3 b.

In P1 of FIG. 7, the barycenter point 55 is substantially on thegrounding point line L. From P2 to P3 in the figure, the weight movestoward the movement direction (right-hand direction in figure), and thebarycenter point 55 moves more on the right-hand side than the groundingpoint line L. The barycenter point 55 is positioned more on theright-hand side than the grounding point line L even in a state wherethe handle 3 b of P4 is started to be turned.

On the basis of P2 to P4 of FIG. 7, the inventors found that thebarycenter point 55 is deviated from the grounding point line L in themovement direction, as a feature of the posture right before thedirection change of the bicycle 3. It should be noted that the sameholds true for a direction change to the left-hand direction. Thedirection change of the bicycle 3 traveling straight toward a back sideon the paper surface can also be predicted in a similar manner.

FIG. 8 is a flowchart showing a processing example by the dangeravoidance apparatus 40. The movement direction of each of the pedestrian2 and the bicycle 3 (rider 4) is predicted by the object movementprediction unit 44 (Step 101). The own-vehicle movement prediction unit45 predicts the movement direction (prediction path) of the automatic100 (Step 102). The danger level judgment unit 46 judges a danger levelof a collision or the like, and the warning output unit 47 outputsdanger avoidance information (Step 103).

FIG. 9 is a flowchart showing an example of an operation of predictingmovement directions of the pedestrian 2 and the bicycle 3. First, theobject detection unit 42 detects each of the pedestrian 2 and thebicycle 3 (Step 201). Next, the posture estimation unit 43 estimates aposture of each of the pedestrian 2 and the bicycle 3 (Step 202).

The barycenter point 55 is calculated as a feature point of each of thepedestrian 2 and the rider 4 (Step 203). Referring to FIG. 4,coordinates of the barycenter point 55 (x_(ave), y_(ave)) are calculatedby the following expressions.

$\begin{matrix}{{x_{ave} = \frac{\sum\limits_{{i = 1},\ldots\mspace{11mu},N}\;\left( {{Wi} \cdot x_{i}} \right)}{W}}{y_{ave} = \frac{\sum\limits_{{i = 1},\ldots\mspace{11mu},N}\left( {{Wi} \cdot y_{i}} \right)}{W}}} & \left\lbrack {{Expression}\mspace{14mu} 1} \right\rbrack\end{matrix}$

It should be noted that the parameters are as follows.

N . . . Number of parts set when estimating framework

Wi . . . Mass of each part

(xi, yi) . . . Positional coordinates of each part

W . . . Total mass of pedestrian and rider (=W₁+ . . . +W_(N))

The mass Wi of each part and the total mass W are preset. For example,an average mass of each part of a human body is used. It should be notedthat it is also possible to distinguish males, females, adults,children, and the like from one another and store each of the masses ofthe respective parts. For example, a type of the pedestrian 2 is judgedfrom an input image, and a mass of each corresponding part is read out.

The positional coordinates of each part are calculated on the basis of aposition of the part estimated by the posture estimation, and a positionof the barycenter point of each part is typically used. It should benoted that the positional coordinates of each part may be calculated onthe basis of positional coordinates of the white circles 51 expressingthe framework. For example, a center point of the white circles 51 atjoint portions at both ends of a part may be used as the positionalcoordinates of the part.

The contact point of the pedestrian 2 or the like with the road surfaceR, that is, the grounding point 56 is calculated (Step 204). For thepedestrian 2, a lowermost point of the estimated framework is calculatedas the grounding point 56. For the bicycle 3, a lowermost point of thewheel 3 a is calculated as the grounding point 56.

Whether the detected target object 1 is a pedestrian 2 is judged (Step205). In a case where the target object 1 is a pedestrian 2 (Yes in Step205), it is judged whether the pedestrian 2 is standing on both feet(Step 206), and in the case of Yes, pedestrian both-feet processing isexecuted (Step 207). In a case where the pedestrian 2 is standing on onefoot (No in Step 206), pedestrian one-foot processing is executed (Step208).

In a case where it is judged in Step 206 that the target object 1 is notthe pedestrian 2 (No), bicycle processing is executed (Step 209).

FIG. 10 is a flowchart showing an example of the pedestrian both-feetprocessing. First, it is judged whether a barycenter is between bothfeet, that is, whether the barycenter point 55 shown in FIG. 5 and thelike is included in the area between the grounding point lines L1 and L2(Step 301). In a case where the barycenter is between both feet (Yes inStep 301), it is judged that the pedestrian 2 is still (Step 302).

It should be noted that in a case where the pedestrian 2 is walkingstraight when a movement history of the pedestrian 2 can be grasped onthe basis of a past input image, or the like, it may be judged that thestraight movement has continued in Step 302.

If the barycenter is not between both feet (No in Step 301), it isjudged whether the barycenter is on the left-hand side of both feet,that is, whether the barycenter point 55 is deviated to the left-handside with respect to the area between the grounding point lines L1 andL2 (Step 303). In a case where the barycenter is deviated to theleft-hand side (Yes in Step 303), it is judged that the pedestrian 2will turn left (Step 304).

It should be noted that it is judged that the pedestrian 2 will turnleft when seen from a direction in which the pedestrian 2 isphotographed by the front camera 11, that is, in front view of theautomobile 100. In a case where the pedestrian 2 is walking toward theautomobile 100 side, the pedestrian 2 him/herself will turn right.

In a case where the barycenter is deviated to the right-hand side (No inStep 303), it is judged that the pedestrian 2 will turn right (Step305). In other words, it is judged that the pedestrian 2 will turn rightin the front view of the automobile 100.

The processing described above becomes as follows when expressed usingcoordinates.

In the case of x_(ground_r)≤x_(ave)≤x_(ground_1), it is judged as astill state

In the case of x_(ave)<x_(ground_r), it is judged as moving in theleft-hand direction

In the case of x_(ave)>x_(ground_1), it is judged as moving in theright-hand direction

It should be noted that x_(ground_1) and x_(ground_r) are x coordinatesof the grounding points 56L and 56R of both feet, respectively.

FIG. 11 is a flowchart showing an example of the pedestrian one-footprocessing. First, it is judged whether the barycenter is above a footon the road surface R, that is, whether the barycenter point 55 shown inFIG. 5 and the like is on the grounding point line (L1 or L2) (Step401). It should be noted that the present invention is not limited tothe case where the barycenter point 55 is strictly positioned on thegrounding point line, and it is also possible to judge that thebarycenter is above a foot in a case where the barycenter is close tothe grounding point line.

For example, a grounding point area that includes a predetermined width(size in x direction) and extends in a y-axis direction about thegrounding point line is set. It is judged that the barycenter is abovethe foot in a case where the barycenter point 55 is included in thegrounding point area.

In a case where the barycenter is above the foot (Yes in Step 401), itis judged that the pedestrian 2 is still (Step 402). In a case where thebarycenter is not above the foot (No in Step 401), it is judged whetherthe barycenter is on the left-hand side of the foot on the ground, thatis, whether the barycenter point 55 is deviated to the left-hand sidewith respect to the grounding point line (or grounding point area) (Step403). In a case where the barycenter is deviated to the left-hand side(Yes in Step 403), it is judged that the pedestrian 2 will turn left(Step 404). In a case where the barycenter is deviated to the right-handside (No in Step 403), it is judged that the pedestrian 2 will turnright (Step 405).

The processing described above becomes as follows when expressed usingcoordinates.

In the case of x_(ave)=X_(ground), it is judged as a still state

In the case of x_(ave)<X_(ground), it is judged as moving in theleft-hand direction

In the case of x_(ave)>X_(ground), it is judged as moving in theright-hand direction

It should be noted that x_(ground) is an x coordinate of the one-footgrounding point (56L or 56R). Further, in a case where the groundingpoint area is set, the judgment is executed using a minimum x coordinateand a maximum x coordinate of the grounding point area as a reference.

FIG. 12 is a flowchart showing an example of the bicycle processing.First, it is judged whether the barycenter is above the grounding point56 of the wheel, that is, whether the barycenter point 55 shown in FIG.7 and the like is on the grounding point line L (Step 501). It should benoted that the present invention is not limited to the case where thebarycenter point 55 is strictly positioned on the grounding point lineL, and it is also possible to judge that the barycenter is above thegrounding point 56 in a case where the barycenter is close to thegrounding point line L. In other words, the grounding point areadescribed above may be set.

In a case where the barycenter is above the grounding point (Yes in Step501), it is judged that the bicycle 3 is running straight (Step 502). Ina case where the barycenter is not above the grounding point 56 (No inStep 501), it is judged whether the barycenter is on the left-hand sideof the grounding point 56, that is, whether the barycenter point 55 isdeviated to the left-hand side with respect to the grounding point lineL1 (or grounding point area) (Step 503). In a case where the barycenteris deviated to the left-hand side (Yes in Step 503), it is judged thatthe bicycle 3 will turn left (Step 504). In a case where the barycenteris deviated to the right-hand side (No in Step 503), it is judged thatthe bicycle 3 will turn right (Step 505).

The processing described above becomes as follows when expressed usingcoordinates.

In the case of x_(ave)=X_(ground), it is judged as a still state

In the case of X_(ave)<x_(ground), it is judged as moving in theleft-hand direction

In the case of X_(ave)>x_(ground), it is judged as moving in theright-hand direction

It should be noted that x_(ground) is an x coordinate of the wheelgrounding point 56. Further, in a case where the grounding point area isset, the judgment is executed using a minimum x coordinate and a maximumx coordinate of the grounding point area as a reference.

By focusing on the position of the barycenter point 55 of the targetobject 1 and the relative positional relationship between the barycenterpoint 55 and the grounding point 56 in this way, it is possible toeasily and highly accurately predict an action of the target object 1.

FIG. 13 is a diagram showing an example of a danger avoidance image asthe danger avoidance information output by the warning output unit 47.In a danger avoidance image 60, a type of each target object 1(pedestrian 2/bicycle 3), a possibility of contact with each targetobject 1, a movement direction 61 of each target object 1, a dangerousarea 62 where there is a possibility of contact, and a danger avoidancepath 63 are displayed.

In the example shown in FIG. 13, an oblique lower-right direction isdisplayed as the movement direction 61 of the bicycle 3 and the rider 4thereof. For example, not only the horizontal direction like themovement direction 61 of the pedestrian 2 but also an oblique directionmay be calculated as the movement direction 61. For example, a specificmovement direction can be calculated on the basis of the position of thebarycenter point 55, the direction of the wheel, and the like.

It should be noted that the direction of the wheel can be calculatedfrom an input image. Further, it is also possible to calculate it by theframework estimation of the bicycle 3 to be described later. Moreover, amovement history of the bicycle that can be calculated on the basis ofpast input images may be used as appropriate.

The dangerous area 62 is an area where there is a possibility of cominginto contact with the automobile 100 in a case where the bicycle 3 orthe like moves along the predicted movement direction 61. For example,the size of the dangerous area 62 is preset for each of the pedestrian 2and the bicycle 3, and the dangerous area 62 is set about the bicycle 3and the like. Alternatively, the dangerous area 62 may be dynamicallyset on the basis of a prediction of a movement speed, acceleration, andthe like of the bicycle 3 and the like.

The danger avoidance path 63 is an image that shows a route for avoidingthe dangerous area 62 displayed for each target object 1 with which itmay collide. For example, a safe avoidance path 63 is calculated using aprediction path predicted by the own-vehicle movement prediction unit 45as a reference. For calculating the danger avoidance path 63, navigationinformation, current location information, road information, or the likemay be used as appropriate.

By displaying the danger avoidance image 60, it becomes possible tocause the driver of the automobile 100 to pay attention to the bicycle 3or the like with which it may collide, and prevent an accidental contactand the like from occurring. Further, by displaying the dangerous area62 and the danger avoidance path 63, the driver can easily grasp a safepath and the like. It should be noted that the driver may be notified ofthe movement direction of each target object 1, the possibility ofcontact, the danger avoidance path 63, and the like by audio.

As described above, in the danger avoidance apparatus 40 of thisembodiment, actions of the pedestrian 2 and the bicycle 3 (rider 4) canbe highly accurately predicted on the basis of estimated postures.Accordingly, it becomes possible to provide effective danger avoidanceinformation for preventing an accident and the like to the driver of theautomobile 100 and the like and thus raise caution. As a result, even ina case where a sudden change in the direction or the like occurs, forexample, regarding the pedestrian 2 or the bicycle 3, an accidentalcontact or the like can be prevented from occurring.

Other Embodiments

The present technology is not limited to the embodiment described above,and various other embodiments can be realized.

For example, in the descriptions above, the prediction of a next actionbased on an estimated posture has been described while taking thedirection change to the left and right as an example. The presenttechnology is not limited to this, and the action prediction may beperformed while focusing on the position of the barycenter point and therelative positional relationship between the barycenter point and thegrounding point in various actions.

FIG. 14 is a diagram for explaining another embodiment of the actionprediction. For example, in a case where the pedestrian 2 or the likesuddenly accelerates, a barycenter of a body is lowered before thesudden acceleration, and the body crouches in many cases. Specifically,the barycenter point 55 is lowered downwardly as shown in P1 and P2 ofFIG. 14, and suddenly accelerates at once in P3. Focusing on a featureof the posture before this sudden acceleration, in a case where thebarycenter point 55 is lowered, it is predicted that a suddenacceleration will be performed.

For judging that the barycenter is being lowered, a position of aframework of a leg portion F or a back portion B may be judged in placeof or in addition to the position of the barycenter point 55.Specifically, whether a leg is bent or a back is bent to crouch the bodymay be judged. By judging in combination with the rightward and leftwardmovements of the barycenter point 55, it is also possible to predict adirection of the sudden acceleration. As a result, it becomes possibleto prevent an accidental contact with the pedestrian 2, the bicycle 3,and the like, that start to run suddenly, from occurring.

Further, a tilt angle of a straight line connecting the barycenter point55 and the grounding point 56 with respect to the road surface R may becalculated. The tilt angle θ can be calculated using, for example, thecoordinates (X_(ave), y_(ave)) of the center point 55 and thecoordinates (x_(ground), y_(ground)) of the grounding point 56. In acase where the tilt angle θ is small, it is judged that the body issufficiently tilted, and thus it can be predicted that a suddenacceleration or a sudden change of direction will be performed.

It is also possible to predict a sudden acceleration or a sudden changeof direction in a case where the pedestrian 2 widely opens his/her legs.

Although already described above, it is possible to highly accuratelypredict an action by acquiring a history of actions of a target objectfrom past input images and using it for predicting the next action.

FIG. 15 is a diagram for explaining another embodiment of the postureestimation of the bicycle 3 and the rider 4 thereof. As shown in FIG.15, a framework may be detected not only for the rider 4 but also forthe bicycle 3. For example, a plurality of parts are preset for thebicycle 3, and a position of each part of the bicycle 3 is estimated onthe basis of an input image. At this time, a technology similar to theframework estimation of the pedestrian 2, the rider 4, and the like maybe used.

If the position of each part of the bicycle 3 is estimated, the whitecircles 51 and lines 52 preset in correspondence with the respectiveparts are set. Accordingly, the posture of the bicycle 3 can beestimated. It is possible to highly accurately estimate an overallposture of the bicycle 3 and the rider 4 thereof on the basis of theestimated posture of each of the bicycle 3 and the rider 4 thereof.

In predicting the next action, an overall barycenter point 95 of thebicycle 3 and the rider 4 thereof is calculated. Further, on the basisof the framework of the bicycle 3, a lowermost point thereof is detectedas the grounding point 56 with the road surface R. On the basis of thesebarycenter point 95 and grounding point 56, an action can be predictedhighly accurately. For example, it becomes possible to estimate asteering amount of the handle 3 b on the basis of the white circles 51 ato 51 e at the wheel portion of the bicycle 3 and specifically predict amovement direction and the like. Further, an action prediction ofextremely high accuracy becomes possible on the basis of the combinationof the posture of the rider 4 and the posture of the bicycle 3.

In the above description, the barycenter point of the target object iscalculated as a feature point for executing the action prediction. Thepresent technology is not limited to this, and a barycenter point of ahead portion or waist portion may be used as the feature point.

The action of the target object existing on the left- or right-hand sideof or behind the automobile may be predicted on the basis ofphotographed images photographed by side cameras on left- and right-handsides, a rear camera, and the like.

The system that includes the automobile including the various camerassuch as a front camera, the sensors, the braking apparatus, and thesteering apparatus and the danger avoidance apparatus according to thepresent technology corresponds to one embodiment of a danger avoidancesystem according to the present technology. Of course, the presenttechnology is not limited to these configurations.

The present technology is applicable to not only automobiles, but alsovarious mobile object apparatuses such as a two-wheeled vehicle and anautomatic three-wheeled vehicle, and is also applicable to varioustechnical fields such as a simulation apparatus thereof and games.Further, the present technology is applicable to not only the mobileobject apparatus but also a monitoring system and the like. For example,it is possible to predict an action of a pedestrian or the like walkingon a bridge, a platform, or the like and notify that person orsurrounding people in a case where there is a danger of falling or thelike.

At least two of the feature portions according to the present technologydescribed above can be combined. In other words, various featureportions described in the respective embodiments may be arbitrarilycombined without distinguishing the embodiments from one another.Moreover, the various effects described above are mere examples andshould not be limited thereto, and other effects may also be exerted.

It should be noted that the present technology can also take thefollowing configurations.

(1) An information processing apparatus, including:

a detection unit that detects a target object from an input image;

an estimation unit that estimates a posture of the detected targetobject; and

a prediction unit that predicts an action of the target object on abasis of the estimated posture.

(2) The information processing apparatus according to (1), in which

the detection unit is capable of detecting a pedestrian from the inputimage, and

the prediction unit predicts an action of the pedestrian on a basis ofan estimated posture of the pedestrian.

(3) The information processing apparatus according to (1) or (2), inwhich

the detection unit is capable of detecting a two-wheeled vehicle and arider thereof from the input image,

the estimation unit estimates at least a posture of the rider, and

the prediction unit predicts an action of the two-wheeled vehicle andthe rider thereof on a basis of the estimated posture of the rider.

(4) The information processing apparatus according to (3), in which

the estimation unit estimates a posture of the two-wheeled vehicle, and

the prediction unit predicts the action of the two-wheeled vehicle andthe rider thereof on a basis of the estimated posture of each of thetwo-wheeled vehicle and the rider thereof.

(5) The information processing apparatus according to any one of (1) to(4), in which

the prediction unit calculates a feature point related to the targetobject on a basis of the estimated posture, and predicts the action ofthe target object on a basis of a position of the calculated featurepoint.

(6) The information processing apparatus according to (5), in which

the feature point is a barycenter point of the target object.

(7) The information processing apparatus according to (5), in which

the detection unit is capable of detecting a two-wheeled vehicle and arider thereof from the input image, and

the prediction unit calculates, as the feature point, a barycenter pointof the rider or an overall barycenter point of the two-wheeled vehicleand the rider thereof.

(8) The information processing apparatus according to any one of (5) to(7), in which

the prediction unit calculates one or more contact points of the targetobject with a road surface on a basis of the estimated posture, andpredicts the action on a basis of a relative positional relationshipbetween the feature point and the one or more contact points.

(9) The information processing apparatus according to any one of (1) to(8), in which

the prediction unit predicts a movement direction of the target object.

(10) The information processing apparatus according to any one of (1) to(9), in which

the prediction unit predicts an abrupt acceleration of the targetobject.

(11) The information processing apparatus according to any one of (1) to(10), in which

the estimation unit estimates a framework of the detected target object.

(12) The information processing apparatus according to any one of (1) to(11), in which

the information processing apparatus is mounted on a mobile objectapparatus, and

the information processing apparatus further includes

an output unit that generates and outputs danger avoidance informationfor avoiding a danger related to a drive of the mobile object apparatuson a basis of the predicted action of the target object.

(13) The information processing apparatus according to (12), in which

the output unit judges a possibility of the mobile object apparatus andthe target object coming into contact with each other, and outputsinformation on the judged possibility.

(14) The information processing apparatus according to (12) or (13), inwhich

the prediction unit is capable of predicting a movement direction of thetarget object, and

the output unit outputs an image including the predicted movementdirection.

(15) The information processing apparatus according to any one of (12)to (14), in which

the output unit outputs an image including a dangerous area where thereis a possibility that the mobile object apparatus and the target objectwill come into contact with each other.

REFERENCE SIGNS LIST

-   -   R road surface    -   L, L1, L2 grounding point line    -   1 target object    -   2 pedestrian    -   3 bicycle    -   4 rider of bicycle    -   40 danger avoidance apparatus    -   41 image acquisition unit    -   42 object detection unit    -   43 posture estimation unit    -   44 object movement prediction unit    -   45 own-vehicle movement prediction unit    -   46 danger level judgment unit    -   47 warning output unit    -   55, 95 barycenter point    -   56, 56L, 56R grounding point    -   60 warning image    -   61 movement direction    -   62 dangerous area    -   63 danger avoidance path    -   100 automobile

The invention claimed is:
 1. An information processing apparatus,comprising: circuitry configured to: detect a target object from aninput image; calculate at least one contact point of the detected targetobject with a road surface; set a ground line in a vertical directionfrom the calculated at least one contact point; determine a relativeposition of a feature point of the target object with respect to the setground line; estimate a posture of the target object based on thedetermined relative position of the feature point of the target object;and predict an action of the target object based on the estimatedposture.
 2. The information processing apparatus according to claim 1,wherein the circuitry is further configured to: detect a pedestrian fromthe input image; and predict an action of the pedestrian based on anestimated posture of the pedestrian.
 3. The information processingapparatus according to claim 1, wherein the circuitry is furtherconfigured to: detect a two-wheeled vehicle and a rider from the inputimage; estimate a posture of the rider; and predict an action of thetwo-wheeled vehicle and an action of the rider, based on the estimatedposture of the rider.
 4. The information processing apparatus accordingto claim 3, wherein the circuitry is further configured to: estimate aposture of the two-wheeled vehicle; and predict the action of thetwo-wheeled vehicle and the action of the rider, based on the estimatedposture of each of the two-wheeled vehicle and the rider.
 5. Theinformation processing apparatus according to claim 1, wherein thefeature point is a barycenter point of the target object.
 6. Theinformation processing apparatus according to claim 1, wherein thecircuitry is further configured to: detect a two-wheeled vehicle and arider from the input image; and calculate, as the feature point, one ofa barycenter point of the rider or an overall barycenter point of thetwo-wheeled vehicle and the rider.
 7. The information processingapparatus according to claim 1, wherein the circuitry is furtherconfigured to predict a movement direction of the target object.
 8. Theinformation processing apparatus according to claim 1, wherein thecircuitry is further configured to predict an abrupt acceleration of thetarget object.
 9. The information processing apparatus according toclaim 1, wherein the circuitry is further configured to estimate aframework of the detected target object.
 10. The information processingapparatus according to claim 1, wherein the information processingapparatus is mounted on a mobile object apparatus, and the circuitry isfurther configured to: generate danger avoidance information, to avoid adanger related to a drive of the mobile object apparatus, based on thepredicted action of the target object; and output the generated dangeravoidance information.
 11. The information processing apparatusaccording to claim 10, wherein the circuitry is further configured to:judge a possibility of a contact of the mobile object apparatus and thetarget object; and output information on the judged possibility.
 12. Theinformation processing apparatus according to claim 10, wherein thecircuitry is further configured to: predict a movement direction of thetarget object; and output an image including the predicted movementdirection.
 13. The information processing apparatus according to claim10, wherein the circuitry is further configured to output an imageincluding a dangerous area, and in the dangerous area, a contact of themobile object apparatus and the target object is possible.
 14. Aninformation processing method, comprising: detecting a target objectfrom an input image; calculating at least one contact point of thedetected target object with a road surface; setting a ground line in avertical direction from the calculated at least one contact point;determining a relative position of a feature point of the target objectwith respect to the set ground line; estimating a posture of the targetobject based on the determined relative position of the feature point ofthe target object; and predicting an action of the target object basedon the estimated posture.
 15. A non-transitory computer-readable mediumhaving stored thereon computer-executable instructions, which whenexecuted by an information processing apparatus, cause the informationprocessing apparatus to execute operations, the operations comprising:detecting a target object from an input image; calculating at least onecontact point of the detected target object with a road surface; settinga ground line in a vertical direction from the calculated at least onecontact point; determining a relative position of a feature point of thetarget object with respect to the set ground line; estimating a postureof the target object based on the determined relative position of thefeature point of the target object; and predicting an action of thetarget object based on the estimated posture.